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Die ISTQB CT-AI Zertifizierung ist den IT-Fachleute eine unentbehrliche Prüfung, weil sie ihres Schicksal bestimmt. Die Fragenkataloge zur ISTQB CT-AI Prüfung brauchen alle Kandidaten. Mit ihr kann der Kandidat sich gut auf die CT-AI Prüfung vorbereiten und nicht so sehr unter Druck stehen. Und die Fragenkataloge in Fast2test sind einzigartig. Mit ihr können Sie die ISTQB CT-AI Prüfung ganz mühlos bestehen.

ISTQB CT-AI Prüfungsplan:

Thema	Einzelheiten
Thema 1	Testing AI-Specific Quality Characteristics: In this section, the topics covered are about the challenges in testing created by the self-learning of AI-based systems.
Thema 2	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Thema 3	Testing AI-Based Systems Overview: In this section, focus is given to how system specifications for AI-based systems can create challenges in testing and explain automation bias and how this affects testing.
Thema 4	Quality Characteristics for AI-Based Systems: This section covers topics covered how to explain the importance of flexibility and adaptability as characteristics of AI-based systems and describes the vitality of managing evolution for AI-based systems. It also covers how to recall the characteristics that make it difficult to use AI-based systems in safety-related applications.
Thema 5	Methods and Techniques for the Testing of AI-Based Systems: In this section, the focus is on explaining how the testing of ML systems can help prevent adversarial attacks and data poisoning.
Thema 6	Introduction to AI: This exam section covers topics such as the AI effect and how it influences the definition of AI. It covers how to distinguish between narrow AI, general AI, and super AI; moreover, the topics covered include describing how standards apply to AI-based systems.

ISTQB Certified Tester AI Testing Exam CT-AI Prüfungsfragen mit Lösungen (Q55-Q60):

55. Frage
Which ONE of the following is the BEST option to optimize the regression test selection and prevent the regression suite from growing large?
SELECT ONE OPTION

A. Using of a random subset of tests.
B. Using an Al-based tool to optimize the regression test suite by analyzing past test results
C. Automating test scripts using Al-based test automation tools.
D. Identifying suitable tests by looking at the complexity of the test cases.

Antwort: B

Begründung:
A . Identifying suitable tests by looking at the complexity of the test cases.
While complexity analysis can help in selecting important test cases, it does not directly address the issue of optimizing the entire regression suite effectively.
B . Using a random subset of tests.
Randomly selecting test cases may miss critical tests and does not ensure an optimized regression suite. This approach lacks a systematic method for ensuring comprehensive coverage.
C . Automating test scripts using AI-based test automation tools.
Automation helps in running tests efficiently but does not inherently optimize the selection of tests to prevent the suite from growing too large.
D . Using an AI-based tool to optimize the regression test suite by analyzing past test results.
This is the most effective approach as AI-based tools can analyze historical test data, identify patterns, and prioritize tests that are more likely to catch defects based on past results. This method ensures an optimized and manageable regression test suite by focusing on the most impactful test cases.
Therefore, the correct answer is D because using an AI-based tool to analyze past test results is the best option to optimize regression test selection and manage the size of the regression suite effectively.

56. Frage
Upon testing a model used to detect rotten tomatoes, the following data was observed by the test engineer, based on certain number of tomato images.

For this confusion matrix which combinations of values of accuracy, recall, and specificity respectively is CORRECT?
SELECT ONE OPTION

A. 1,0.87,0.84
B. 0.84.1,0.9
C. 0.87.0.9. 0.84
D. 1,0.9, 0.8

Antwort: C

Begründung:
To calculate the accuracy, recall, and specificity from the confusion matrix provided, we use the following formulas:
* Confusion Matrix:
* Actually Rotten: 45 (True Positive), 8 (False Positive)
* Actually Fresh: 5 (False Negative), 42 (True Negative)
* Accuracy:
* Accuracy is the proportion of true results (both true positives and true negatives) in the total population.
* Formula: Accuracy=TP+TNTP+TN+FP+FN ext{Accuracy} = rac{TP + TN}{TP + TN + FP + FN}Accuracy=TP+TN+FP+FNTP+TN
* Calculation: Accuracy=45+4245+42+8+5=87100=0.87 ext{Accuracy} = rac{45 + 42}{45 + 42
+ 8 + 5} = rac{87}{100} = 0.87Accuracy=45+42+8+545+42=10087=0.87
* Recall (Sensitivity):
* Recall is the proportion of true positive results in the total actual positives.
* Formula: Recall=TPTP+FN ext{Recall} = rac{TP}{TP + FN}Recall=TP+FNTP
* Calculation: Recall=4545+5=4550=0.9 ext{Recall} = rac{45}{45 + 5} = rac{45}{50} = 0.9 Recall=45+545=5045=0.9
* Specificity:
* Specificity is the proportion of true negative results in the total actual negatives.
* Formula: Specificity=TNTN+FP ext{Specificity} = rac{TN}{TN + FP}Specificity=TN+FPTN
* Calculation: Specificity=4242+8=4250=0.84 ext{Specificity} = rac{42}{42 + 8} = rac{42}{50} = 0.84Specificity=42+842=5042=0.84 Therefore, the correct combinations of accuracy, recall, and specificity are 0.87, 0.9, and 0.84 respectively.
References:
* ISTQB CT-AI Syllabus, Section 5.1, Confusion Matrix, provides detailed formulas and explanations for calculating various metrics including accuracy, recall, and specificity.
* "ML Functional Performance Metrics" (ISTQB CT-AI Syllabus, Section 5).

57. Frage
A motorcycle engine repair shop owner wants to detect a leaking exhaust valve and fix it before it falls and causes catastrophic damage to the engine. The shop developed and trained a predictive model with historical data files from known health engines and ones which experienced a catastrophic fails due to exhaust valve failure. The shop evaluated 200 engines using this model and then disassembled the engines to assess the true state of the valves, recording the results in the confusion matrix below.
What is the precision of this predictive model

A. 90.0%
B. 94.5%
C. 94.2%
D. 98.9%

Antwort: C

Begründung:
Precision is a performance metric used to evaluate the accuracy of positive predictions in a classification model. It is defined by the formula:
Precision=TPTP+FP×100% ext{Precision} = rac{TP}{TP + FP} imes 100%Precision=TP+FPTP×100% Where:
* TP (True Positives)= Number of correctly predicted positive cases
* FP (False Positives)= Number of incorrectly predicted positive cases
The confusion matrix provided in the question would typically list these values. Based on ISTQB's guidelines for calculating precision, selecting the correct number of true positives and false positives from the given data should yield94.2%as the precision.
* Section 5.1 - Confusion Matrix and ML Functional Performance Metricsexplains the calculation of precisionusing the confusion matrix.
Reference from ISTQB Certified Tester AI Testing Study Guide:

58. Frage
The stakeholders of a machine learning model have confirmed that they understand the objective and purpose of the model, and ensured that the proposed model aligns with their business priorities. They have also selected a framework and a machine learning model that they will be using.
What should be the next step to progress along the machine learning workflow?

A. Agree on defined acceptance criteria for the machine learning model
B. Evaluate the selection of the framework and the model
C. Tune the machine learning algorithm based on objectives and business priorities
D. Prepare and pre-process the data that will be used to train and test the model

Antwort: C

Begründung:
Themachine learning (ML) workflowfollows a structured sequence of steps. Once stakeholders have agreed on theobjectives, business priorities, and the framework/model selection, the next logical step is to prepare and pre-process the databefore training the model.
* Data Preparationis crucial becausemachine learning models rely heavily on the quality of input data. Poor data can result in biased, inaccurate, or unreliable models.
* The process involvesdata acquisition, cleaning, transformation, augmentation, and feature engineering.
* Preparing the dataensures it is in the right format, free from errors, and representative of the problem domain, leading to better generalization in training.
* A (Tune the ML Algorithm):Hyperparameter tuning occursafter the model has been trainedand evaluated.
* C (Agree on Acceptance Criteria):Acceptance criteria should already have been defined in theinitial objective-setting phasebefore framework and model selection.
* D (Evaluate the Framework and Model):The selection of the framework and ML model has already been completed. The next step isdata preparation, not reevaluation.
* ISTQB CT-AI Syllabus (Section 3.2: ML Workflow - Data Preparation Phase)
* "Data preparation comprises data acquisition, pre-processing, and feature engineering.
Exploratory data analysis (EDA) may be performed alongside these activities".
* "The data used to train, tune, and test the model must be representative of the operational data that will be used by the model".
Why Other Options Are Incorrect:Supporting References from ISTQB Certified Tester AI Testing Study Guide:Conclusion:Since the model selection is complete, thenext step in the ML workflow is to prepare and pre-process the datato ensure it is ready for training and testing. Thus, thecorrect answer is B.

59. Frage
Consider an AI system in which the complex internal structure has been generated by another software system. Why would the tester choose to do black-box testing on this particular system?

A. The tester wishes to better understand the logic of the software used to create the internal structure.
B. The black-box testing method will allow the tester to check the transparency of the algorithm used to create the internal structure.
C. Black-box testing eliminates the need for the tester to understand the internal structure of the AI system.
D. Test automation can be built quickly and easily from the test cases developed during black-box testing.

Antwort: C

Begründung:
In AI-based systems, particularly those where theinternal structure has been generated by another software system, the complexity often makes it difficult for human testers to analyze the inner workings. As per the ISTQB Certified Tester AI Testing (CT-AI) Syllabus:
* Black-box testingis particularly useful when dealing with AI systems that have been generated by another system because:
* It allows testingwithout requiring knowledge of the internal logic.
* The AI model may be too complex for human testers to comprehend, making white-box testing ineffective.
* Black-box testing evaluates theinputs and outputs, ensuring functional correctnesswithout needing insight into how the system reaches a decision.
* Why other options are incorrect?
* A (Test automation and black-box testing): While automation is possible,black-box testing is not primarily about automationbut aboutabstracting the internal complexity.
* B (Understanding the logic of the software): This contradicts the premise of black-box testing, which is designed totest functionality without needing to understandthe inner workings.
* C (Checking transparency of the algorithm):Black-box testing does not check algorithm transparency-that would requirewhite-box testing or explainability techniques.
Thus, the best choice isOption D, as black-box testingremoves the need to analyze the internal structure of AI systems, making it the most appropriate testing method in this case.
Certified Tester AI Testing Study Guide References:
* ISTQB CT-AI Syllabus v1.0, Section 8.5 (Challenges Testing Complex AI-Based Systems)
* ISTQB CT-AI Syllabus v1.0, Section 8.6 (Testing the Transparency, Interpretability, and Explainability of AI-Based Systems)

60. Frage
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Die ISTQB CT-AI Zertifizierungsprüfung ist schon eine der beliebten IT-Zertifizierungsprüfungen geworden. Aber für die Prüfung braucht man viel Zeit und Energie, um die Fachkenntnisse gut zu beherrschen. Im diesem Zeitalter, wo die Zeit sehr geschätzt wird, betrachtet man Zeit wie Geld. Das Schulungsprogramm zur ISTQB CT-AI Zertifizierungsprüfung von Fast2test dauert ungefähr 20 Stunden. Dann können Sie Ihre Fachkenntnisse konsolidierern und sich gut auf die ISTQB CT-AI Zertifizierungsprüfung vorbereiten.

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